Evaluation of the impacts of different treatments of spatio-temporal variation in catch-per-unit-effort standardization models

Grüss, Arnaud; Walter, John F.; Babcock, Elizabeth A.; Forrestal, Francesca C.; Thorson, James T.; Lauretta, Matthew V.; Schirripa, Michael J.

doi:10.1016/j.fishres.2019.01.008

Cited by 60 publications

(28 citation statements)

References 56 publications

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“…The representation of spatial and spatio-temporal variation in spatio-temporal models results in more precise statistical inference and, therefore, in the delivery of more reliable scientific advice to stock and habitat assessments and resource management (Grüss, Walter, et al, 2019;Thorson et al, 2015).…”

Section: Discussionmentioning

confidence: 99%

“…This spatial autocorrelation is modeled via spatial variation terms that represent spatial variation that is stable over time and spatio‐temporal variation terms that represent spatial variation that changes between years (Grüss et al., 2017; Thorson, 2019a). The representation of spatial and spatio‐temporal variation in spatio‐temporal models results in more precise statistical inference and, therefore, in the delivery of more reliable scientific advice to stock and habitat assessments and resource management (Grüss, Walter, et al, 2019; Thorson et al., 2015).…”

Section: Discussionmentioning

confidence: 99%

“…We relied on delta‐Gamma GLMMs because the yellow croaker biomass catch rate data that we employed in this study included many zeros (Thorson, Shelton, Ward, & Skaug, 2015). In other words, we first fitted a binomial GLMM to encounter/non‐encounter data for yellow croaker, then fitted a Gamma GLMM to positive biomass catch rate data, and finally multiplied the predictions of the binomial and Gamma GLMMs to obtain final biomass density estimates for yellow croaker (Grüss, Walter, et al, 2019; Lo, Jacobson, & Squire, 1992). The spatio‐temporal delta‐Gamma GLMMs were implemented using R package “VAST” (Grüss, Rose, Justić, & Wang, 2020; Grüss, Thorson, et al, 2020; Thorson, 2019a), which is publicly available online (https://github.com/James-Thorson-NOAA/VAST).…”

Section: Methodsmentioning

confidence: 99%

“…Most of the studies that have investigated patterns of distribution shifts have estimated changes in the centers of gravity (COGs) of fish populations directly from monitoring data (e.g., Dulvy et al., 2008; Engelhard, Righton, & Pinnegar, 2014; Perry et al., 2005; Pinsky et al., 2013). In other studies, spatio‐temporal models were employed to compute northward and eastward COGs for understanding patterns of distribution shifts, as well as the effective area occupied of fish populations for understanding patterns of range expansion/contraction (e.g., Grüss & Thorson, 2019; Grüss, Walter, et al, 2019; Thorson Pinsky & Ward, 2016; Thorson, Rindorf, Gao, Hanselman, & Winker, 2016).…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Understanding patterns of distribution shifts and range expansion/contraction for small yellow croaker (Larimichthys polyactis) in the Yellow Sea

Han

Grüss

Shan

et al. 2020

Fisheries Oceanography

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Detecting and analyzing patterns of distribution shifts and range expansion/contraction is important to understand population dynamics and changes in stock status. Here, we develop a spatio-temporal model for yellow croaker (Larimichthys polyactis), which was fitted to bottom trawl survey biomass data collected in the Yellow Sea in the winter of 2001-2017. The model accounts for both spatial and spatio-temporal structure and can potentially include the effects of surface temperature and of an annual index, the Pacific Decadal Oscillation, represented using a recently developed spatially varying coefficient model. We selected a spatio-temporal model with no covariates based on Akaike's information criterion. The center of gravity for yellow croaker was found to move northwest between 2001 and 2010, and then southwest over the period 2010-2017. These results reflected the predicted progressive disappearance of yellow croaker density hotspots (i.e., highest density areas) in the north and southeast areas of the Yellow Sea, which resulted in the central area of the Yellow Sea becoming the only yellow croaker density hotspot in 2017. This finding has important implications for fisheries management in the context of the China-South Korea fisheries agreement, as it indicates a measurable displacement of yellow croaker biomass toward China. The exclusion of covariates from the spatio-temporal model was not expected a priori and may be due to the facts that environmental

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Understanding patterns of distribution shifts and range expansion/contraction for small yellow croaker (Larimichthys polyactis) in the Yellow Sea

Han

Grüss

Shan

et al. 2020

Fisheries Oceanography

Self Cite

View full text Add to dashboard Cite

show abstract

“…Thus, VAST provides a better way to fill a missing stratum than assuming it has zero biomass, and it is preferable to ad hoc imputation for transparency and reproducibility considerations. The performance of VAST for models such as this has been tested previously through simulation (Brodie et al, 2020; Grüss & Thorson, 2019; Grüss et al, 2019; Johnson et al, 2019; Thorson et al, 2015), and future simulation experiments exploring performance for the combined surveys method proposed here are recommended. Specifically, future work could explore the accuracy of VAST imputed biomass in these cases.…”

Section: Discussionmentioning

confidence: 99%

Quota allocation for stocks that span multiple management zones: analysis with a vector autoregressive spatiotemporal model

Adams

Brooks

Legault

et al. 2021

Fisheries Management Eco

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Allocating quotas among stakeholders requires an agreed‐upon formula. If the stock unit spans multiple management jurisdictions, the formula may require updated biomass estimates of the stock's spatial distribution with respect to those jurisdictions. Data for calculating stock biomass often come from fishery‐independent surveys. While stratified random sampling is a common design, strata boundaries may not always align with state or national jurisdictions, requiring post hoc stratification and imputation to calculate area‐specific biomass. The vector autoregressive spatiotemporal (VAST) model was explored as a tool for calculating fish biomass within subareas of a defined stock unit for three different stocks jointly managed by the United States and Canada on Georges Bank in the Northwest Atlantic Ocean. VAST estimated proportions of stock biomass in each nation's waters were compared with an existing allocation algorithm that utilises a loess smooth through the average design‐based swept area biomass from three fishery‐independent surveys. The ability of VAST to impute biomass when no tows occur in a subarea of a survey stratum was demonstrated, as well as the relatively smoother biomass trend compared with design‐based estimates, which may be desirable if the intent is to avoid large inter‐annual swings in allocated quota.

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Spatiotemporal Variability of Fishery‐Dependent Indices for the Declining Louisiana Southern Flounder Fishery

Smith

Dance

West

et al. 2021

N American J Fish Manag

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In recent years, management agencies across the Gulf of Mexico and southern U.S. Atlantic have recognized Southern Flounder Paralichthys lethostigma as a declining fish stock. Population declines in coastal Louisiana are exhibited by indices of recruitment and biomass, which have reached levels that present management concerns. To develop a better understanding of this declining fishery, we examined fishery-dependent data collected by the Louisiana Department of Wildlife and Fisheries' recreational angler harvest survey (referred to as "LA Creel"). Data were modeled using generalized additive models to estimate temporal components of recreational Southern Flounder landings in both seasonality and trend. Over the study period (2014-2019), recreational landings exhibited a declining trend statewide. Strong seasonal peaks in the fall occurred statewide and regionally in every coastal management zone (i.e., estuary). Understanding the current fishery with the fine-scale resolution provided by the LA Creel survey can be used to help guide future management decisions in the pursuit of a sustainable management strategy inclusive of fishery-dependent information.

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Evaluation of the impacts of different treatments of spatio-temporal variation in catch-per-unit-effort standardization models

Cited by 60 publications

References 56 publications

Understanding patterns of distribution shifts and range expansion/contraction for small yellow croaker (Larimichthys polyactis) in the Yellow Sea

Understanding patterns of distribution shifts and range expansion/contraction for small yellow croaker (Larimichthys polyactis) in the Yellow Sea

Quota allocation for stocks that span multiple management zones: analysis with a vector autoregressive spatiotemporal model

Spatiotemporal Variability of Fishery‐Dependent Indices for the Declining Louisiana Southern Flounder Fishery

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